A Literature Survey of Cooperative Caching in Content Distribution Networks

Content distribution networks (CDNs) which serve to deliver web objects (e.g., documents, applications, music and video, etc.) have seen tremendous growth since its emergence. To minimize the retrieving delay experienced by a user with a request for a web object, caching strategies are often applied - contents are replicated at edges of the network which is closer to the user such that the network distance between the user and the object is reduced. In this literature survey, evolution of caching is studied. A recent research paper [15] in the field of large-scale caching for CDN was chosen to be the anchor paper which serves as a guide to the topic. Research studies after and relevant to the anchor paper are also analyzed to better evaluate the statements and results of the anchor paper and more importantly, to obtain an unbiased view of the large scale collaborate caching systems as a whole.Comment: 5 pages, 5 figure

Egocentric online social networks: Analysis of key features and prediction of tie strength in Facebook

The widespread use of online social networks, such as Facebook and Twitter, is generating a growing amount of accessible data concerning social relationships. The aim of this work is twofold. First, we present a detailed analysis of a real Facebook data set aimed at characterising the properties of human social relationships in online environments. We find that certain properties of online social networks appear to be similar to those found ?offline? (i.e., on human social networks maintained without the use of social networking sites). Our experimental results indicate that on Facebook there is a limited number of social relationships an individual can actively maintain and this number is close to the well-known Dunbar?s number (150) found in offline social networks. Second, we also present a number of linear models that predict tie strength (the key figure to quantitatively represent the importance of social relationships) from a reduced set of observable Facebook variables. Specifically, we are able to predict with good accuracy (i.e., higher than 80%) the strength of social ties by exploiting only four variables describing different aspects of users interaction on Facebook. We find that the recency of contact between individuals ? used in other studies as the unique estimator of tie strength ? has the highest relevance in the prediction of tie strength. Nevertheless, using it in combination with other observable quantities, such as indices about the social similarity between people, can lead to more accurate prediction

Cooperative content replication in networks with autonomous nodes

We examine a network where autonomous nodes participate in content exchange or delivery, and define a replication group as a set of nodes that cooperate in order to effectively retrieve information objects from a distant server. Each node locally replicates a subset of the server objects and can access objects stored by other nodes in the group at a smaller cost, compared to the cost of accessing them from the server. Given that nodes are autonomous and independently decide which objects to replicate, the problem is to construct efficient distributed algorithms for content replication that induce low average access cost. This problem becomes even more challenging when the group has to deal with churn, i.e., random "join" and "leave" events of nodes in the group; churn induces instability and has a major impact on cooperation efficiency. Given a probability estimate of each node being available, we propose a distributed churn-aware object placement strategy. By considering a game-theoretic approach, we identify cases where the churn-aware strategy is individually rational for all nodes, while the churn-unaware is not. Numerical results further show that the algorithm outperforms, in most cases, its churn-unaware counterpart, and allows for a more fair treatment of nodes according to their availability frequency, thus inciting nodes to cooperate. © 2011 Elsevier B.V. All rights reserved